Neuroimmunological diseases of the central nervous system (CNS) represent a broad spectrum of diverse diagnoses, most of which are considered rare disorders. The pathophysiology of these diseases is poorly understood and effective therapies are sporadic. Additionally, the diagnosis of varied neuroimmunological disorders and their differentiation from the most common one, multiple sclerosis (MS), is not trivial. Indeed, current diagnostic criteria of MS are based on clinical phenomenology and CNS structural imaging, both of which are not specific for MS. Therefore, MS diagnostic criteria require excluding alternative diagnoses. Medical fields that successfully transitioned from phenomenological to molecular diagnoses (e.g. oncology or infectious diseases) accelerated development of effective therapies. This is because molecular nosology of diseases and development of diagnostic tools that allow measurements of different pathogenic mechanisms in individual patients are prerequisites for precision medicine. Without understanding patient-specific driver(s) of the disease, clinicians may prescribe therapies for which a patient lacks target(s), incurring not only unacceptable societal costs, but also exposing patients to side-effects of applied therapies without their potential for benefit. Analogously, if a patients expression of a disease encompasses more than one pathophysiological mechanism, such patient requires combination treatments that target all contributing processes. Thus, we view establishment of molecular nosology of neuroimmunological diseases and development of molecular tests that can distinguish and quantify different pathogenic processes that lead to destruction of the CNS tissue as essential for therapeutic advances in neuroimmunology and neurology in general. This project studies intrathecal immune responses and pathogenic mechanisms in patients referred to Neuroimmunological Diseases Unit (NDU) for diagnostic work-ups of neuroimmunological CNS disorders. The goal of this study is to define the mechanisms underlying the development of disability in immune-mediated disorders of the CNS and to distinguish these from physiological (and often beneficial) responses of the human immune system to CNS injury. We have established natural history protocol (09-N-0032) under which all untreated patients with suspected immune-mediated disorders of the CNS undergo detailed evaluation at NDU, consisting of the collection of clinical and paraclinical quantitative measures of disease activity, severity and disability, standard and novel quantitative neuroimaging markers and novel immunological (cellular and molecular) biomarkers originating from cerebrospinal fluid (CSF). These ex-vivo studies are supplemented with in-vitro co-culture models that allow us to define cellular origin of novel, clinically-validated biomarkers and, ideally, their function and role in disease process. All patients are coded and analysis of paraclinical, neuroimaging and molecular biomarkers are performed in an unbiased (i.e. blinded) fashion to define which biomarkers are associated with specific neuroimmunological disease or phenotype. We use machine learning and mathematical modeling applied to training cohorts and validate novel diagnostic classifiers, models, clinical or imaging scales, in the independent validation cohorts. The long-term objective of this study is to acquire knowledge that: 1. Allow precise determination of pathogenic processes that underlie expression of a neuroimmunological diseases in individual patients; and 2. Allows precise therapeutic targeting of each identified disease mechanism. This is the basis of precision neurology, whereby we can therapeutically inhibit all pathogenic mechanisms and enhance repair mechanisms to minimize the extent of CNS tissue damage. Currently we focus on studies of MS, with other neuroimmunological diseases serving as controls for MS studies. Short-term goal is to identify genes, proteins, pathways that underlie MS activity (i.e., presence and frequency of MS relapses), progression (i.e., the amount of CNS tissue destruction reflected by imaging biomarkers/scales and clinical disability) and, most importantly, MS severity (i.e., speed of accrual of disability in time). We also strive to understand whether current, FDA-approved treatment of MS are curative, what mechanisms underlie residual disease activity, how to best measure it, and how to therapeutically target it. This work led us to conclude that current clinical scales (i.e., clinical measured of MS disability and severity) are simply too insensitive to allow detection and validation of small effect sizes that individual genes or proteins exert on disease process. Therefore, we temporarily re-focused on development (and validation) of scales of clinical utility with significantly enhanced sensitivities and accuracies, by combining novel technologies, such as App development on mobile platforms (smartphones and tablets) with machine learning strategies.